Package: D2C
Type: Package
Title: Predicting Causal Direction from Dependency Features
Version: 1.2.1
Date: 2015-01-14
Author: Gianluca Bontempi, Catharina Olsen, Maxime Flauder
Maintainer: Catharina Olsen <colsen@ulb.ac.be>
Description: The relationship between statistical dependency and causality lies
    at the heart of all statistical approaches to causal inference. The D2C
    package implements a supervised machine learning approach to infer the
    existence of a directed causal link between two variables in multivariate
    settings with n>2 variables. The approach relies on the asymmetry of some
    conditional (in)dependence relations between the members of the Markov
    blankets of two variables causally connected. The D2C algorithm predicts
    the existence of a direct causal link between two variables in a
    multivariate setting by (i) creating a set of of features of the
    relationship based on asymmetric descriptors of the multivariate dependency
    and (ii) using a classifier to learn a mapping between the features and the
    presence of a causal link
License: Artistic-2.0
Depends: R(>= 2.10.0), randomForest
Imports: gRbase, lazy, RBGL, MASS, corpcor, methods, Rgraphviz, foreach
LazyData: true
Packaged: 2015-01-20 15:26:57 UTC; bontempi
Suggests: knitr
VignetteBuilder: knitr
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2015-01-21 00:23:55
